Let's Stick Together? The Impact of Collectivism on COVID-19 Spread and the Effectiveness of Non-Pharmaceutical Government Interventions

TABLE OF CONTENTS

1. Extracting Data

1.1. Extracting indicators from the World Bank

1.2. Merging metrics, cases, and Hofstede index

1.2.1. Extracting case data

1.2.2. Hofstede Index

1.2.3. Merging data

1.3. Mobility Data

1.3.1. Extracting mobility data

1.3.2. Plotting mobility data

1.4. Testing data

1.5. Governmental response

1.6. Regime Type

1.7. Distance from WUHAN

2. COVID-19 and Individualism EDA

2.1. Getting COVID-19 data per month

2.2. Getting COVID-19 data by month for a number of countries

3. Cleaning the data

3.1. Dropping missing countries

3.2. Calculating the number of cases for neighboring countries that month

3.3. Changing variable names, adding interactions

4. Results section for the paper

4.1. General table

4.2. Individualism score

4.2.1. Overall Individualism

4.2.2. Cumulative Individualism

4.2.3. Relationship between all variables

4.2.4. Relationship between government intervention and individualism

5. Running regressions

5.1. Regressions with total cases as of date

5.1.1. Getting interaction effects

5.1.2. Results with Tanzania

5.1.3. Results without Tanzania

5.2. Regression for different months

5.3. Regression for different seasons

5.3.1. Regression model summaries

5.3.2. Regression model summaries with robust standard errors and without Tanzania

5.3.3. Regression models with Tanzania

5.3.4. Without Tanzania

5.3.4.1. No Continents

5.3.4.2. Continents

5.3.5. Regression without an interaction effect

6. Diagnostics

6.1. Regression with total cases

6.1.1. Homoscedasticity of residuals + functional misspecification

6.1.1.1. Checking autumn data

6.1.1.2. Checking summer data

6.1.1.3. Diagnostics for all seasons

6.1.2. Multicollinearity

6.1.3. Homoscedasticity of residuals

6.1.4. Autocorrelation of residuals

6.1.5. Features and residuals are uncorrelated

6.1.6. Outliers

6.1.7. High leverage points

6.1.8. Normality of residuals

7. Additional Checks

7.1. Sensitivity check

7.2. Check on average government intervention

7.3. MCAR test

7.4. GDP Calculations

7.5. Stringency

7.6. Differences in COVID-19 rates and response

1. Extracting data

1.1. Extracting indicators from the World Bank

1.2. Merging metrics, cases, and hofstede index

1.2.1. Extracting case data

1.2.2. Hofstede index

1.2.3. Merging the data

1.3. Mobility data

1.3.1. Extracting the data

1.3.2. Plotting mobility data

1.4. Testing data

1.5. Governmental response

1.6. Regime Type

1.7. Distance from Wuhan

2. COVID-19 and Individualism EDA

2.1. Getting COVID-19 data per month

2.2. Getting COVID-19 data by month for a number of countries

3. Cleaning the data

3.1. Dropping missing countries

3.2. Calculating the number of cases for neighboring countries that month

These countries don't have the metric, so take the continent average

3.3. Changing variable names, adding interaction

4. Results section for the paper

4.1. General table

4.2. Individualism scores

4.2.1. Overall individualism

4.2.2. Cumulative individualism

4.2.3. Relationship between all variables

4.2.4. Relationship between government intervention and invidualism

5. Running regressions

5.1. Regression with total cases as of date

5.1.1. Getting interaction effects

5.1.2. Results with Tanzania

5.1.3. Results without Tanzania

5.2. Regression for different months

5.3. Regression for different seasons

5.3.1. Regression model summaries

5.3.2. Regression model summaries with robust standard errors and without Tanzania

5.3.3. Regression models with Tanzania

5.3.4. Without Tanzania

5.3.4.1. No continents

5.3.4.2. Continents

5.3.5. Regression without an interaction effect

6. Diagnostics

6.1. Regression with total cases

6.1.1. Homoscedasticity of residuals + functional misspecification

6.1.1.1. Checking autumn data

Running the model without Tanzania

6.1.1.2. Checking summer data

Running the model without Tanzania

6.1.1.3. Diagnostics for all seasons

6.1.2. Multicolinearity

6.1.2.1.Overall model

6.1.3. Homoscedasticity of residuals

6.1.4. Autocorrelation of residuals

There is no autocorrelation is the residuals

According to the sample size and number of regressors, the statistics should be in the range of 1.369 and 1.910 as presented by the DW statistical table. This means that the model is not suffering from serial correlation in the residuals.

6.1.5. Features and residuals are uncorrelated

There is no relationship between any of the predictors and the error term.

6.1.6. Outliers

Results do not change after checking for outliers

6.1.7. High leverage points

Singapore, the point with high leverage, does not change any of the conclusions reached

6.1.8. Normality of residuals

7. Additional checks

7.1. Sensitivity check

7.2. Check on average government intervention

7.3. MCAR test

7.4. GDP calculations

7.5. Stringency

7.6. Differences in COVID-19 rates and response